When members of a fish species are segregated into multiple
reproductive stocks, allele frequencies at neutral genetic markers diverge
under genetic drift such that the variance in gene frequencies reflects the
magnitude of reproductive isolation among these stocks. Thus, gene frequency
differences among geographic samples can be used to indirectly estimate
patterns of gene flow and hence stock structure of the species. Molecular
markers have been used to infer stock structure in fishes for over forty years
(Utter, 1991). A brief glossary of genetic terms is included at the end of this
section for those readers who may be less familiar with the subject.

Application of molecular markers to the
estimation of stock structure in marine elasmobranchs can be challenging for
several reasons. Genetic stock structure is less pronounced in marine species,
which experience few barriers to migration, than in freshwater species (Ward,
Woodwark and Skibinski, 1994). Stock structure is especially weak in highly
mobile pelagic fishes (Waples, 1998). Further, sharks exhibit relatively low
levels of genetic variation at some molecular markers, perhaps owing to a slowed
mutation rate and, or, low long-term effective population sizes (Martin, Naylor
and Palumbi, 1992; Smith, 1986). Markers that are not sufficiently variable
will not provide the necessary data for a statistically powerful test of stock
structure and fish from two geographic regions that are fixed for the same
allele may not necessarily be members of the same stock.

The choice of molecular marker depends
on the quality and type of tissue available as well as the equipment and
expertise. Even a small amount of reproductive migration among stocks is
sufficient to prevent genetic divergence at neutral molecular markers. Thus,
stocks that are independent from the fisheries perspective may exhibit
negligible genetic differentiation (Waples, 1998). Traditional tag and
recapture studies performed in concert with molecular genetics studies can
provide more information than either approach can individually.

5.2 ESTIMATING STOCK
STRUCTURE WITH MOLECULES

The degree to which stocks are reproductively isolated is
typically estimated using various forms of Sewell Wright’s FST statistic (Wright, 1931). In the case of a co-dominant
locus that exhibits only two alleles FST is equal to

(5.1)

where Ht is the expected heterozygosity in the
population based on the mean allele frequency across populations and
expectations of Hardy-Weinberg equilibrium (i.e. Ht = 2pq where p = the frequency of one allele and q = 1-p) and HS is the mean heterozygosity within populations. Thus, the
greater the variance in allele frequencies among populations the greater the
deficit of heterozygosity within each population. FST can be determined directly from the variance in allele
frequencies as:

(5.2)

where Var(p) is the variance in the frequency of an allele among
subpopulations. Expected values of FST range from zero when each sample possesses identical gene
frequencies and hence there is a single genetic stock, to unity when isolated
stocks are fixed for alternate alleles.

Either of these measures is sensitive to
sampling error and in the absence of distinct stocks will result in positive FST values, the magnitudes of which are inversely
proportional to sample size. Waples (1998) observed that in highly migratory
species, such as many sharks, the magnitudes of FST estimates resulting from sampling error alone may be larger than
the parametric FST values among stocks. Various unbiased
estimators of Wright’s FST include Weir and Cockerham’s θ
estimator, which includes corrections for several types of sampling error and
sometimes produces negative FST estimates when the true value of FST is small or zero (Weir and Cockerham, 1984). Many recent
studies employ analysis of molecular variation (AMOVA) (Excoffier, Smouse and
Quattro, 1992), which provides an unbiased estimator of FST known as ΦST and also
permits partitioning of genetic variation to multiple hierarchical levels.
These estimators are computationally demanding but are incorporated into a
variety of freely available software packages. Statistical tests of the
hypothesis that ΦST = 0 (and hence samples are drawn from a
single genetic stock) are calculated using algorithms that either model or
resample the data and determine the significance level of ΦST as the likelihood that a larger ΦST value could be produced via a random allocation of the
genotypes or alleles (Rousset, 2001).

Several software packages are freely
available for analyzing molecular genetic data including Arlequin
(http://lgb.unige.ch/arlequin/) (Schneider, Roessli and Excoffier, 2000),
Genepop (http://wbiomed.curtin.edu.au/genepop/) (Raymond and Rousset, 1995),
and GDA (http://lewis.eeb.uconn.edu/lewishome/software.html) (Lewis and Zaykin,
2001). The capabilities of these and several other programs were recently
reviewed by Labate (2000). Arlequin can be downloaded in Microsoft Windows,
Macintosh or Linux format and can handle haploid (e.g. mtDNA) as well as
diploid (allozyme and microsatellite) data. Genepop can be downloaded to run in
a windows environment or can be run directly from the web page. GDA is only
available in windows format and determines significance of θ by
bootstrapping across loci, which is only applicable to studies that employ a
large number of loci. Under the assumptions of the island model of migration
(Wright, 1931), which assumes a large number of discrete populations with equal
amounts of migration among each population, FST can be related to migration as:

(5.3)

where Nem is the product of the effective
population size and the migration rate. Nem can be thought of as the effective number of migrants, that is
the number of reproductive animals exchanged among populations. It may seem
counterintuitive that the magnitude of FST would be related to the number of migrants and not migration
rate. However, the degree to which allele frequencies among isolated
populations diverge due to genetic drift is inversely proportional to the
effective population size. Thus, populations with a large Ne require a smaller migration rate to produce the same
magnitude of genetic variance among populations (FST ). The relationship implies several simplifying assumptions that
are unrealistic for shark populations (e.g. equal migration among each of the
many populations). However, deviations from these assumptions have only minor
effects on the relationship between FST and Nem. For example, the more realistic case
of increased migration among geographically proximate locations and a small
number of populations produces slightly lower FST values for the same rate of migration (Mills and Allendorf
1996).

Mitochondrial (mt) DNA is potentially a
more powerful marker than nuclear DNA. Because mtDNA is maternally inherited as
a haploid molecule it has approximately ¼ the effective population size of a
nuclear marker (Birky, Marayama and Fuerst, 1983). The relationship between FST and migration is

(5.4)

where Nemf refers to the effective migration rate of females only. In
species with equal rates of male and female migration the magnitude of FST will be greater for mitochondrial markers than nuclear
markers. Further, because of the smaller effective population size mtDNA
reaches equilibrium levels of FST more
quickly and thus a recently established pattern of stock structure will be more
accurately represented by mitochondrial data than by nuclear DNA data. In
species that exhibit female reproductive philopatry and outcrossing with males
from widespread localities, such as several species of marine mammals and sea
turtles, mtDNA exhibits stronger differentiation than nuclear markers (Karl and
Bowen, 1992; Palumbi and Baker, 1994; and Gladden et al., 1999). However, the
differences in the rates of genetic drift, mutation and intraspecific variation
among mitochondrial and nuclear markers are sufficient to produce vast
differences in estimates of FST between the marker types without any
differences in male and female-mediated gene flow (Buonaccorsi, McDowell and
Graves, 2001). Thus, larger FST values for mitochondrial markers
relative to nuclear markers do not necessarily indicate female philopatry
against a backdrop of male roaming.

5.3 MOLECULAR MARKERS

5.3.1 Marker type

Several types of molecular markers have been applied to the
estimation of stock structure in sharks and many other types used in other
marine fishes have yet to be employed in elasmobranchs. The choice of marker
depends on the experience of the researcher and the types of equipment and the
types and quality of tissue that are available. It is unfeasible to provide
specific protocols here, but several excellent published volumes containing
protocols for these and other techniques include Hillis, Moritz and Mable
(1996), Ferraris and Palumbi (1996) and Hoelzel (1998).

5.3.2 Allozymes

Allozymes were the first molecular markers to gain widespread
use for distinguishing stocks of fishes (Utter, 1991). Allozymes are distinct
allelic forms of enzymes that are separated by charge and in some cases
three-dimensional shape on a separatory medium, typically starch gels,
polyacrylamide gels or cellulose acetate plates and visualized with
histochemical stains that indicate the migration of molecules with specific
enzyme activities (Murphy et al., 1996; May, 2003). Allozymes degrade rapidly
after death, especially at high temperatures, and the use of allozymes as
molecular markers requires fresh or frozen tissue (maintained att -20°C or
preferably colder). Because tissue types vary in enzyme expression, it is often
useful to collect multiple tissue types (e.g. white muscle, heart, liver,
brain) to score a large number of loci. Thus, allozyme electrophoresis is not
the best technique where lethal sampling and immediate freezing (e.g. with dry
ice or liquid nitrogen) of tissue samples are not possible.

Resolution of allozyme banding patterns
requires considerable interpretation (Buth, 1990). Homozygotes for different
alleles produce single bands with varying motilities while heterozygotes take
on an appearance that is determined by the subunit structure of the active
enzyme. Monomeric enzymes produce two-banded heterozygotes while dimeric and
tetrameric enzymes (those possessing two and four peptides per active enzyme)
exhibit three-and five-banded heterozygotes. Many enzymatic reactions are
catalyzed by products of multiple loci heteropolymers, which can further
complicate the banding patterns. Resolution of allozyme patterns as discrete
bands rather than smears requires the screening of multiple running buffer
conditions to identify the optimal conditions for each locus.

Several studies of allozymes have
detected low levels of variation in sharks. In the first published study of
allozymes in sharks Smith (1986) reported relatively low variation in seven
species. Low levels of allozyme variation and geographic heterogeneity in
carcharhinid sharks were observed by Lavery and Shaklee (1989) and by Heist,
Graves and Musick (1995). Relatively high levels of heterozygosity and
heterogeneity were found in Pacific angel sharks (Squatina californica) (Gaida,
1997) and gummy sharks (Mustelus antarcticus) (Gardner and Ward, 2002).

Resolution of allozyme loci can be more
of an art than a science and variation in the methodology and experience among
labs result in differences in the amount of variation that can be resolved on
allozyme gels. Gardner and Ward (1998) found that on average 25.5% of allozyme
loci in gummy shark were polymorphic with a mean heterozygosity of 0.099. For
the same species over a somewhat smaller geographic range MacDonald (1988)
detected variation in only one of 32 presumed loci (3%) with a mean
heterozygosity of 0.006 in the same species. Certainly some of this discrepancy
must be due to the increased resolution of the study by Gardner and Ward.

The relative simplicity of the materials
needed to perform the allozyme technique (i.e. many rigs are “homemade”) make allozymes an attractive tool for
labs with little research funding. However, as PCR-based techniques are
becoming more affordable, the low variation and high tissue quality demands of
allozymes make techniques that score variation at the DNA-level more
attractive. Plans for allozyme equipment can be found in Aebersold et al.(1987)
and Murphy et al.(1996).

5.3.3 Mitochondrial
DNA

Mitochondrial DNA of elasmobranchs and other fishes is a single
closed loop of double stranded DNA approximately 16500 base pairs (bp) in length and presumably inherited only from
the maternal parent (Billington, 2003). The haploid, uniparental inheritance of
mtDNA results in a fourfold reduction in the effective population size and
therefore an accelerated rate of genetic drift, which in turn increases the
rate and magnitude of genetic differentiation among isolated fishery stocks
(Birky, Marayama and Fuerst, 1983). Data derived from sequencing or restriction
fragment length polymorphism (RFLP) analysis of mtDNA permit estimation of the
relative divergence time of any two mtDNA haplotypes and can be used to provide
evidence of deep historic divisions or cryptic species (Figure 5.1).

If relatively large quantities (several
grams) of fresh or ultrafrozen tissue and an ultracentrifuge are available,
mtDNA can be isolated in its pure circular form and be subjected to restriction
enzymes that cleave the circular DNA at specific four-to six-base motifs. The
resultant population of restriction fragments can be resolved on agarose or
polyacrylamide gels and seen using radiolabeling or UV illumination of ethidium
bromide stained bands (Figure 5.1). This technique was performed by Heist,
Graves and Musick (1995) Heist, Musick and Graves (1996a, 1996b) on sandbar
(Carcharhinus plumbeus), shortfin mako (Isurus oxyrinchus) and Atlantic
sharpnose (Rhizoprionodon terraenovae) sharks. In the sharpnose shark study,
whole molecule mtDNA prepared from tiger shark was used to probe southern blots
of Atlantic sharpnose shark hearts that did not provide sufficient
whole-molecule mtDNA.

FIGURE 5.1Mitochondrial DNA variation in shortfin mako (Isurus
oxyrinchus). Lane “S”is a size standard lane. Numbers at left refer to the size
(in base pairs) of each size standard. Whole molecule mtDNA digested with the
restriction enzyme Bst E II produces two haplotypes (A and B). Haplotype
“A”differs from “B”in that a fragment of approximately 7000 base pairs in “B”is
digested into two smaller fragments of approximately 4400 and 2600 in “A”.

With the advent of PCR more studies are
employing restriction digestion or sequencing of discrete regions of mtDNA.
Perhaps the most useful region for analyzing stock structure in elasmobranchs
is the D-loop or control region, which contains the largest stretches of
noncoding DNA in the elasmobranch mtDNA genome and in many fishes studied it
exhibits the highest nucleotide substitution rate, presumably due to the lack
of purifying selection. In my lab we routinely use a primer designed by Martin
and Palumbi (1993) located in the cytochrome-b protein coding region (CB6H
5’CTC CAG TCT TCG RCT TAC AAG where “R”represents equal quantities of A and G)
and a mammalian primer designed in the highly-conserved 12S ribosomal gene
(282 5’AAG GCT AGG ACC AAA CCT) (J.C. Patton, LGL Alaska Research Associates,
Anchorage, USA, unpublished data) to amplify the entire D-loop region in a
variety of sharks. The resultant PCR product can then be analysed using
restriction enzymes or direct sequencing. The widespread availability of
inexpensive thermal cyclers and gel rigs make PCR-RFLP a viable method of
analysis for labs with a limited research budget.

The genetic diversity present in mtDNA
can be represented as haplotype diversity which is estimated as

(5.5)

where h is the haplotype diversity, n is the number of
individuals scored, xi is the frequency of each allele, and l
is the number of unique haplotypes detected (Nei and Tajima 1981). This
equation is essentially the same as that for estimating heterozygosity at a
diploid locus and can be thought of as the likelihood that two randomly sampled
haplotypes differ. Because haplotype diversity is affected by the number of
bases surveyed (i.e. amount of sequence data or number of restriction enzymes
employed) a more universal gauge of variation is nucleotide sequence diversity
(δ) which can be estimated as

(5.6)

Where and are the frequencies of haplotypes i and j and
is the genetic distance between each pair of
haplotypes (Nei and Tajima 1981). AMOVA (Excoffier, Smouse and Quattro, 1992)
can then be used to estimate ΦST by partitioning the genetic diversity into among and between
sample components. The REAP software package (McElroy et al., 1991), which is
available at http://bioweb.wku.edu/faculty/mcelroy/, can be used to estimate ŏ
and to construct a distance matrix between haplotypes for AMOVA.

Sharks possess relatively low levels of
intraspecific mtDNA heterogeneity, presumably due to the low rate of mtDNA
evolution relative to that of other vertebrates (Martin, Naylor and Palumbi,
1992). Levels of nucleotide sequence diversity based on whole-molecule RFLP in
sharks range from 0.036% in sandbar shark (Heist, Graves and Musick 1995) to
0.347% in shortfin mako (Heist, Musick and Graves, 1996a). To detect a
sufficient amount of variation one must either perform the whole-molecule
technique with a large number (e.g. eight or more) restriction enzymes or
perform direct sequencing. In our lab we are sequencing the entire mtDNA D-loop
in blacktip sharks (C. limbatus) to produce a haplotype diversity of 0.71
(Keeney et al., 2003).

5.3.4 Microsatellites

DNA microsatellites are among the most recent types of markers
developed for estimating stock structure. These are highly repetitive segments
of nuclear DNA that are amplified via PCR and typically resolved on
polyacrylamide gels (O’Connell and Wright, 1997). Microsatellite alleles differ
in size based upon differences in the number of repeat units present. Alleles
differ in size by multiples of the core repeat motif (typically two to four
bases) and thus high resolution is required to score microsatellites. Typically
PCR products are end-labeled with radionuclides (e.g. 32P or 33P) and resolved via autoradiography
(Figure 5.2) or fluorescently tagged and resolved on automated DNA sequencers.
Both of these techniques may be beyond the capabilities of labs with limited
budgets and, or without access to radionuclides.

The major hurdle to scoring
microsatellites in any species is the development of PCR primers that amplify
polymorphic loci. To date polymorphic microsatellite loci have been developed
in sandbar shark (Heist and Gold, 1999), white shark (Carcharodon carcharias)
(Pardini et al., 2000), lemon shark (Negaprion brevirostris) (Feldheim, Gruber
and Ashley, 2001a; 2001b), shortfin mako (Schrey and Heist, 2002) and nurse
shark (Ginglymostoma cirratum) (Heist et al., 2003). Primers developed in one
species often work on congeners and sometimes members of related genera but
either fail to amplify or amplify only monomorphic products in other families
or in more distantly-related taxa. Of sixteen polymorphic microsatellite loci
developed from the blacktip shark between five and eleven loci were polymorphic
in each of ten other species of Carcharhinus and several loci were polymorphic
in tiger shark, lemon shark, blue shark (Prionace glauca), Atlantic sharpnose
shark and two species of hammerhead sharks (Sphyrna spp.) (Keeney and Heist,
2003). Primers developed in shortfin mako amplified polymorphic microsatellites
in salmon (Lamna ditropis), porbeagle (L. nasus) and white sharks (Schrey and
Heist,2002).

Microsatellite data are analysed much
like allozyme data although the high heterozygosity and large number of alleles
(e.g. 20 or more) can cause a deflation of FST (Hedrick, 1999). Microsatellites evolve via mutational increases
and decreases in the number of times the core motif is repeated in each allele.
Thus, microsatellites exhibit a finite number of alleles and alleles are often
shared even among completely isolated gene pools (e.g. among species). The
maximum value FST can be expected to achieve is equal to
homozygosity, which for loci with 20 or more alleles may be less than 0.05.
Thus, the maximum value that can be achieved for FST is comparable to the expected error associated with measurements
involving small sample sizes (Waples, 1998). An obvious way to alleviate some
of this problem is to employ loci with moderate numbers of alleles and moderate
heterozygosities and to obtain sufficiently large sample sizes to reduce the
error in estimates of FST .

A common problem that attends the high
genetic diversity of microsatellites and the sampling power of modern
estimators is the detection of small but statistically significant FST values. Low but significant FST values can arise through a small amount of gene flow (e.g. 1–10
individuals per generation) between stocks that are essentially discrete in
terms of recruitment, or they can be an artifact of sampling (e.g. inclusion of
close relatives in a sample) and scoring (e.g. null alleles) and thus
constitute a Type I error. Dizon, Taylor and O’Corry-Crowe (1995) warned that
the consequences of failing to reject the null hypothesis of FST =0 when it is false (Type II error) may be more
deleterious to the management of a species than falsely concluding that
multiple stocks are present and recommended that power analyses be used to
adjust the rejection (α) level upward to a level that balanced the effects
of both types of statistical error. Feldheim, Gruber and Ashley (2001b)
concluded that a statistically significant (p< 0.05) θ value of 0.016
based on highly polymorphic (Heterozygosity = 0.69 to 0.90) microsatellite loci
was too low to consider lemon sharks from the Florida, the Bahamas and Brazil
as distinct stocks. Tagging data (Kohler, Casey and Turner, 1998) indicate that
lemon sharks move between the Bahamas and Florida, but no lemon sharks tagged
in either Florida or the Bahamas moved to the Caribbean or beyond. Thus, it
seems unlikely that lemon sharks from Florida and Brazil do not comprise
distinct fishery stocks. While gene flow has apparently been high enough to
prevent evolutionary divergence among lemon sharks in the western Atlantic,
statistically significant differences in allele frequency, regardless of their
magnitude, indicate that samples are drawn from different populations (Knutsen
et al., 2003).

5.3.5 Other markers

Several other types of molecular markers are used to assess
stock structure in fishes but have yet to be applied to elasmobranchs. Random
Amplified Polymorphic DNA (RAPD) employs one or more short primers (typically
about ten bases) to amplify a population of fragments that are resolved on
agarose or polyacrylamide gels (Hadrys, Balcik and Schierwater, 1992). The
degree to which bands are shared among individuals can be used to assess the
relatedness of individuals within and among populations. While this method is
attractive because it does not require taxon-specific primers as mtDNA RFLP and
microsatellites do, there are several serious shortcomings that have prevented
this technique from gaining widespread acceptance as a tool for analysis of
stock structure. PCR is a finicky process that often produces inconsistent
results, especially with short primers and low annealing temperatures. Whether
a faint band is present or absent may depend on the quality of the tissue used
to prepare the DNA or the dynamics of the specific PCR reaction that produced
the profile. If tissue quality varies among sample locations, there can be a systematic
bias in the data leading to an erroneous conclusion of stock structure.

Another technique, Amplified Fragment
Length Polymorphism (AFLP) analysis, is performed by attaching oligonucleotide
adapters to nuclear DNA restriction fragments and amplifying with longer PCR
primers that anneal mostly to the adapters, but also the first of three bases
of the genomic DNA (Vos et al., 1995). While this approach is far more work
than RAPD, the data are more repeatable because of the use of longer PCR
primers and higher annealing temperatures. Both RAPD and AFLP produce dominant
data (i.e. there is generally no way to distinguish between bands that are
present in heterozygous or homozygous dosages) and as a result statistical
treatment of the data are not as powerful as those for co-dominant data (e.g.
allozymes and microsatellites).

5.3.6 Tissue
collection

The kinds of tissue samples available and the method of
preservation determine what kinds of molecular markers can be used. PCR-based
methods are most forgiving and can even be performed on dried fins (Shivji et
al., 2002). For PCR-based analyses we routinely collect fin clips using a
scalpel to excise approximately 0.5 cm2 from the trailing edge of the first dorsal fin. The thin
trailing edge of the fin produces far better yields of DNA than do muscle
tissue or thick skin from other parts of the body. Fin clips can be stored in
either 95% ethanol or 20% dimethyl sulfoxide saturated with NaCl. Tissues are
stable in either medium at room temperature for several months, however long
term storage of ethanolpreserved tissues is best done at 4°C or colder. Tissues
for whole molecule RFLP need to be kept fresh or frozen once and not subjected
to freeze-thaw cycles as each freezing cycle produces ice crystals that
linearize the mitochondria making mtDNA purification difficult. Tissues for
allozymes are most demanding in that enzymes degrade rapidly after death.
Tissues need to be frozen (preferable in dry ice or liquid nitrogen) and
maintained as cold as possible until homogenized for electrophoresis.

Many sharks undergo seasonal and
reproductive migrations and may segregate by sex and life stage. Thus, a
careful choice of where, when and from which animals to collect tissue can
influence the outcome of a study. For example, in the study of blacktip sharks
described by Keeney et al. (2003) all tissues were collected from neonate
sharks near or within continental nursery areas. Thus, any signal resulting
from reproductive philopatry could be filtered from the noise of adult
movement. Such studies can be biased because a sample from a single nursery may
contain siblings, which would tend to inflate estimates of gene frequency
differences among samples. However, because sharks such as the blacktip have
low fecundities and do not reproduce every year, the number of potential
sibling pairs is low and comparisons across sequential years can be used to
determine whether a sampling of siblings is influencing estimates of FST .

5.4 SELECTED CASE
STUDIES

5.4.1 Gummy shark

The gummy shark is a small coastal species continuously
distributed around the southern two-thirds of Australia. Gardner and Ward
(1998) found statistically significant differences in allozyme allele and mtDNA
haplotype frequencies in gummy sharks collected from the southern and
southeastern coasts of Australia including Tasmania. Measures of GST (an analog of FST ) were significantly greater than the values expected due to
sampling error for three of seven polymorphic loci and for RFLP haplotypes of
whole-molecule mtDNA. Both molecular markers indicated that gummy sharks from
the southern coast of Australia, ranging from Bunbury to Eden and including
Tasmania, comprised a single stock while gummy sharks from the east coast of
Australia from Eden north comprised one or more additional stocks. Vertebral
counts did not differ throughout southern Australia. However, there appeared to
be a gradual increase in the number of precaudal vertebrae corresponding to
decreasing latitude on the east coast. Thus, despite the continuous
distribution and great potential for movement of M. anarcticus, there exist
multiple fishery stocks in Australian waters. Subsequently, Gardner and Ward
(2002) reported data from additional Mustelus species including M. lenticulatus
from New Zealand and two putative but undescribed species from Australia.
Allozyme, mtDNA, and vertebral count data all confirmed the presence of four
species of Mustelus in the waters of Australia and New Zealand.

5.4.2 Blacktip shark

The blacktip shark is a migratory species that is the most
important component of the US longline shark fishery operating in the
southeastern United States in the Atlantic Ocean and Gulf of Mexico. Neonate
blacktip sharks from the west coast of Florida migrate south in the fall,
presumably to southern Florida, and have been shown to return to specific
nursery areas in subsequent years (Keeney et al., 2003). Whether adult females
return to their natal nurseries for parturition is unknown. A study of mtDNA
sequences and microsatellites in young-of-the-year blacktip sharks collected
from four nursery areas: west coast of Florida, South Carolina, Texas and
Mexican Yucatan revealed significant heterogeneity in mtDNA (FST = 0.111, p<0.001) but not microsatellite loci (FST < 0.001, P=0.316) (Hueter et al., 2005). Neither
marker revealed significant differences among three Florida nurseries separated
by less than 250 km. Thus, blacktip sharks comprise multiple fishery stocks in
US and Mexican waters and while females may tend to return to natal nurseries
the fidelity to do so is not high enough to result in significant structuring
among proximal nurseries.

5.4.3 White shark

The white shark is a wide-ranging globally distributed species
with populations clustered around localities with abundant marine mammals.
Pardini et al. (2001) compared mtDNA and nuclear (microsatellite) markers in
white sharks from South Africa, Australia and New Zealand. The mtDNA data
indicated two divergent clusters of haplotypes that were nearly clustered into
two highly divergent clades. One clade (type A) was found in 48 of 49
individuals surveyed in Australia and New Zealand while the other clade was
found in 39 individuals from South Africa and in one of the 49 individuals
surveyed in the Australia-New Zealand sample. FST analogs (θ) based on five microsatellite loci were all
non-significant. Based on the discrepancy in estimates of stock structure
between nuclear and mitochondrial data Pardini et al. (2001) concluded that
female white sharks are much more philopatric than males.

5.4.4 Shortfin mako

The shortfin mako is a highly migratory cosmopolitan species
found throughout the Atlantic, Pacific and Indian Oceans. Heist, Musick and
Graves (1996a) examined whole molecule mtDNA RFLP data in 120 shortfin makos
from the North Atlantic (US and Canada), South Atlantic (Brazil), North Pacific
(California) and South Pacific (Australia) and found small but significant
differences in haplotype frequencies between the North Atlantic and all other
samples. Subsequently, Schrey and Heist (2003) examined microsatellites in 433
mako sharks including the individuals described in Heist, Musick and Graves
(1996a). They also reanalysed the data from Heist, Musick and Graves (1996a) using
a more powerful statistical approach. Among-ocean-basin FST estimates from the mitochondrial data were significant and two
orders of magnitude larger than the estimates of FST based on microsatellites. A power analysis indicated that if the
amount of heterogeneity present in the mtDNA data accurately represented the
magnitude of gene flow of both sexes a statistically significant FST would have been detected using microsatellites, assuming
that the stock structure was stable long enough for nuclear markers to reach
equilibrium. The discrepancy in the levels of resolution in mtDNA and
microsatellites is likely due to sex-biased dispersal, but they could also be
influenced by differences in the resolving powers of the two markers. The
shortfin mako results differed from those of white sharks (Pardini et al.,
2002) in that no strong phylogeographic signal is present in the mtDNA data,
only minor frequency differences among locations. Shortfin mako does not
comprise a single worldwide population, but there has been a sufficient amount
of historical migration among ocean basins to make detection of stock structure
using molecular markers (and especially nuclear DNA markers) challenging.

5.5 CONCLUSIONS

Using molecular markers to estimate stock structure in sharks can
be challenging owing to the great potential for migration among shark stocks,
the difficulty in detecting genuine but small differences in gene frequencies
in the presence of recent or episodic migration among stocks and inappropriate
(too low or too high) levels of variation provided by some molecular markers.
Nevertheless several studies have ably demonstrated stocks in sharks and even
in highly migratory species across seemingly continuous distributions.
Comparisons between markers with different modes of inheritance (e.g. nuclear
vs. mitochondrial) may indicate differences in male-versus female-mediated
gene flow. Because many sharks are viviparous k-strategists that produce
well-formed young at a time and place conducive to survival, stocks that overlap
during part of the year may segregate into discrete stocks for mating and, or
parturition. Thus, a careful selection of where and when tissues are collected
(e.g. from neonates in nursery areas) coupled with a wise choice of a molecular
marker can provide valuable information about the stock structure of sharks
that cannot be obtained from other methods.

Molecular detection of stock structure
is a complementary technique to tagging and morphology based studies of stock
structure. While tagging reveals gross movements of individuals, genetics
measures the flow of genes over many generations and can be used to study
fidelity to nursery or breeding grounds in animals whose distributions may
sometimes overlap with those of other stocks. Morphological and life history
differences may be due to different environmental influences and hence may, or
may not, be reflected in gene frequency differences at neutral loci.

5.6 GLOSSARY OF
GENETIC TERMS USED

Allele -Alternate forms of a gene at a
particular locus. Each diploid organism may possess either one (homozygote) or
two (heterozygote) alleles at a locus; however, there may be more than two
alleles in a population.

Co-dominant markers -Markers that
exhibit both alleles in a heterozygous state. Co-dominant markers are more
powerful than dominant markers in which a heterozygous individual is
indistinguishable from an individual homozygous for the dominant allele.

FST -An index of the magnitude of allele
frequency difference among populations. At a locus with two alleles the maximum
value of FST is unity and occurs when each population bears only a single
allele not found in any other population. If allele frequencies are identical
across populations, FST= 0.

Genetic drift -Random change in gene
frequencies due to random stochastic sampling of alleles from generation to
generation.

Heterozygosity -The fraction of
individuals that exhibit two different alleles at a locus or alternately the
fraction of loci over which an individual exhibits two different alleles.

Heterozygous -Possessing two different
alleles at a locus.

Homozygous -Possessing two identical
alleles at a locus.

Locus -A particular location on a
chromosome where a gene or other DNA sequence resides. Diploid organisms
possess two copies of each locus that may exhibit either the same (homozygote)
or different (heterozygote) alleles.

Mitochondrial DNA (mtDNA) -DNA found in
the mitochondria in cells. In animals, including sharks, mtDNA is a double
stranded molecule approximately 16500 base pairs in length. Mitochondrial DNA
is inherited strictly from the female parent and thus is a haploid (one copy
per cell) marker.

Molecular marker -A polymorphic
heritable trait that can be scored for variation within or between species.

Neutral genetic markers -Polymorphic
genetic traits that are presumed not to be influenced by natural selection and
thus are sensitive only to mutation, migration and genetic drift. Most models
that relate gene frequency differences with stock structure assume that the
markers examined are selectively neutral.

Nuclear DNA -The vast majority of DNA in
animal cells is found in the nucleus. Nuclear DNA is equally inherited from
both parents and thus is a diploid (two copies per cell) marker.

Polymerase Chain Reaction (PCR) -A
technique for producing millions of copies of a chosen segment of DNA by repeatedly
annealing sequence-specific primers on either side of the region of interest
and performing (typically) thirty or more cycles of DNA synthesis. This
procedure allows the characterization of a particular segment of nuclear or
mitochondrial DNA using only minute amounts of tissue.

Lewis, P.O. & Zaykin, D. 2001.
Genetic Data Analysis: Computer program for the analysis of allelic data.
Version 1.0 (d16c). Free program distributed by the authors over the internet
from http://lewis.eeb.uconn.edu/lewishome/software.html.